Static power reduction in caches using deterministic naps

ABSTRACT

Disclosed embodiments relate to a dNap architecture that accurately transitions cache lines to full power state before an access to them. This ensures that there are no additional delays due to waking up drowsy lines. Only cache lines that are determined by the DMC to be accessed in the immediate future are fully powered while others are put in drowsy mode. As a result, we are able to significantly reduce leakage power with no cache performance degradation and minimal hardware overhead, especially at higher associativities. Up to 92% static/Leakage power savings are accomplished with minimal hardware overhead and no performance tradeoff.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 15/431,922, filed Feb. 14, 2017, which is adivisional of and claims priority to U.S. patent application Ser. No.14/694,285, filed Apr. 23, 2015, which claims the benefit of relatedU.S. Provisional Application Ser. No. 61/983,216, filed Apr. 23, 2014,all of which are incorporated by reference herein in their entireties.

TECHNICAL FIELD

The technical field of this disclosure relates to cache memory for dataprocessors.

BACKGROUND

Phased caches were previously introduced as cache architecture to reducethe redundant and high-energy consumption caused by reading all dataways on every cache access even though only one of them will be used ifthe access hits the cache. Phased caches do not query the data arrays inthe first cycle of access but rather, wait until a hit is determinedbefore accessing the specific data way hit. This saves dynamic readenergy but static energy consumption is not reduced since both the tagand data arrays are ON throughout the program execution.

The rapid increase in microprocessor speed has exceeded the rate ofimprovement in DRAM (Dynamic Random Access Memory) speed in recentyears. This widening performance gap between processors and memories hascreated several challenges for computer designers since memoryperformance can easily become a bottleneck to overall systemperformance. Specifically, processor performance has been observed toincrease at about 60% annually, while memory systems lag significantlybehind at about 10% annual improvement. To solve this problem, designersturn to memory performance improvements which ultimately dictate theperformance and power consumption of processors.

Caching is a common approach used to achieve memory system speed up, bystoring data that has been recently used in faster memory. Therefore,using a larger cache could increase the access hit rate, which in turnimproves processor speed but this comes at a cost—increased hardware andhigher static and dynamic energy consumption.

As a result, there is usually a trade-off between energy and performancein memory system design, since not all accessed memory locations can bestored in faster memories such as caches. Current memory systemsdesigned with SRAMs, DRAMs and/or CAMs, have not been able to catch upwith processor performance. As a result, larger caches are oftenemployed in memory systems to bridge this memory processor performancegap. While these large caches offer improved performance, they alsoincrease the power consumed by the processor. An alternative to improveperformance is associativity, but it also leads to increased powerconsumption due to parallel querying of multiple tags. This increasingcache power consumption resulting from the drive for improvedperformance, cannot be overlooked because caches contribute asignificant fraction of the overall power consumed by modern processors.Several authors have concluded that cache/memory systems contribute30-60% of the total power consumed by processors.

Reducing cache size in an attempt to save power is not an option either,because it leads to higher miss rates and effectively more energyconsumption. As a result, several attempts have been made to reducevoltages and design lower power circuits to reduce the high proportionof power consumed by caches/memory systems. However, these circuit leveltechniques have not been very successful; rather, power dissipationlevels have steadily increased with each new microprocessor generation,leading to a renewed interest in architectural approaches that reducethe switching capacitive power component of memory systems withoutsacrificing performance. In an attempt to save power, some researchershave directed their architectural improvements at better performancebecause of the observation that improved performance (i.e. less misses)usually lead to less power consumption. Others focus on power reductiontechniques targeted at specific aspects of the architecture, with sometrade off in performance.

SUMMARY

This invention deterministically powers ON only data RAM lines or groupof lines that will be accessed in the immediate future while keeping allother lines powered down. The tag RAMS remain ON to avoid any extralatency associated with powering on a tag RAM/line that is to beaccessed. The data RAM on the other hand is deterministically powered ONbefore access with no extra latency. This is possible in phased cachesbecause hit determination takes a minimum of 2-cycles before data RAMaccess. Therefore, the power-ON sequence for a set/set-group istriggered on every access to the set/group. Once the hit/miss isdetermined in the second cycle, all ways of the set will be ON, thenpower down begins for all other ways except the matched (hit/miss) way.The powered ON set/ways are kept ON until the request has beencompletely processed. All outstanding accesses, in all pipe stages andbuffers contribute to the overall power ON state of an individual cacheline or group of lines they belong. When way information becomesavailable, all other ways not scheduled to be read/written are alsopowered down if no other member of that set or power group of sets needsthe way ON.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects of this invention are illustrated in thedrawings, in which:

FIG. 1 shows a memory controller module;

FIG. 2 shows implementation in a high performance 2-pipe stagearchitecture;

FIG. 3 shows implementation in a low power 3 pipe stage architecture;

FIG. 4 shows a cache organized with power groups;

FIG. 5 shows leakage power savings in an L1I cache;

FIG. 6 shows leakage power savings in an L1D cache;

FIG. 7 shows leakage power savings in an L1D cache;

FIG. 8 shows static power savings in an L1I cache; and

FIG. 9 shows static power savings in a 4 core processor.

DETAILED DESCRIPTION

The deterministic napping technique shown in this invention reducesstatic/leakage power in caches by leveraging the ability to retainmemory contents at low power states. This technique also takes advantageof the fact that data RAMS do not have to be read in the first cycle ofcache access while the lines of the referenced set are beingtransitioned to full power state. These data RAM accesses can occurafter tag RAM reads during hit/miss determination or even a cycle afteras in phased cache architectures. Unlike conventional drowsy caches,which keep most lines of the data RAM in a low power state, and onlyrestores full power when an access occurs to such low powered lines, thedNap architecture, maintains cache lines that will be accessed in theimmediate future, in a fully powered state. This ensures accesses arenever stalled while a wake up is being triggered. As a result, dNapcaches do not suffer from the performance degradation incurred byconventional drowsy caches, due to accesses to a low powered line. Theproposed approach is specifically focused on deterministic naps in onlythe data RAMS, for two main reasons. First, data RAMS are known to bemuch larger than the tag RAMS, therefore, they contribute a majorportion of static energy. Second, cache accesses are non-deterministicand can occur at any time, starting with a tag RAM read. Therefore, thetag RAMS are always fully powered to avoid delays due to waking anapping tag line.

A Memory Nap Controller (MNC) is used to track in-flight cachetransition fully powered lines to a low power napping state. The fullpower transition is always completed ahead of data RAM access with noextra latency incurred at the time of access. This is enabled bydelaying data RAM accesses by 1 or 2 cycles after tag RAM read dependingon architecture pipe-line. All current and outstanding accesses, in allpipeline stages and buffers contribute to the overall power ON state ofany individual cache line. FIG. 1 shows how S×2^(s) decoders are used todetermine the accessed sets in different cache stages (where S isbit-width of the address' “Set” field) and the contribution of eachstage to the final power enable (PE) of each set. The n pipe-stages 101and m buffers 102 shown in FIG. 1 typically vary by architecture. The Sset bits 103 of each pipe stage, are decoded in decoders 104 to activatean L=2^(s) bit, one hot value representing the specific set decoded. Allbits representing a set across the cache pipe-stages and buffers areORed in gates 104 and 105 to determine which set(s) must be fullypowered. For example, all bit-Os of all decoder outputs are fed into OR0in FIG. 1 to control set 0 power (signal PE0 106). A nap is initiatedwhen a PE transitions from 1 to 0, while wake up is triggered on atransition from 0 to 1. These transitions complete within 1 clock cycleeven when waking a larger 512 KB cache. Further static power savings areachievable by transitioning all other ways to a drowsy state afterhit/miss determination has chosen a single cache way. If a new accessoccurs to a set that is attempting to transition unaccessed ways todrowsy state, the new access takes precedence and keeps the line fullypowered. The fully powered cache lines are kept in that state until therequest has been completely processed. Multiple cache lines can make adNap power group 107 to reduce the MNC hardware overhead while takingadvantage of the built-in low power feature available in custom SRAMmemory arrays. For example, power groups of eight, controlled by powergroup enables (PGEs) as seen in FIG. 1 can be configured. Thiseliminates the need to use the (log₂w) LSBs of the set field in thecontroller logic, thereby reducing the decoder sizes and the number ofOR gates significantly, leading to reduced MNC hardware overhead.

The ease of integrating the dNap architecture with existing cachearchitectures is discussed as it relates to both high performance andlow power architectures. First, FIG. 2 shows how the dNap cacheintegrates into an existing two pipe stage high performancearchitecture. On a new cache access, the set field 201 of address isdecoded in decoder 202, followed by tag RAM 203 reading during the firstpipe-stage 204. Data RAM reads are delayed to the next pipe-stage 205.Wake up from nap state is also triggered immediately after set decodewhile tag RAMS are being read. Second, FIG. 3 shows how the dNap cacheintegrates into an existing three pipe stage low power phased cachearchitecture. Wake up is triggered after decode of set field 301 as intheir high performance counterparts, the difference being this low powerarchitecture allows up to 2 pipe stages 302 and 303 to complete wake-up.

Deterministic napping at the individual cache line allows the maximumnumber data RAM lines to be kept in nap state, given individual cacheline power can now be controlled independently. But this may not beeasily achievable in some existing cache architectures which already usememory arrays that group multiple cache lines together for power andarea savings. This is usually the case with vendor supplied memoryarrays.

To enable the use of existing low power retention available in vendormemories, and to make the deterministic nap architecture more robust,deterministic napping is extended to contiguous cache line groups. Thechoice of contiguous cache lines is due to the spatial locality ofaccess which suggests that the next contiguous line will most likely beaccessed after the current one. Therefore, keeping these contiguouscache lines in the same power state benefits from the proposeddeterministic napping scheme by reducing the triggers to transitionbetween nap and full power states by the dNap power controller. Forexample, FIG. 4 shows an example of grouping eight cache lines 401 tosingle power group 402; this requires additional OR level for OR′ing thepreviously OR reduced outputs of sets 0 through 7 as seen in FIG. 1.

Vendor supplied memories that already have a built-in low powerretention state benefit more from this scheme because they do not needany extra nap state logic per cache line. The trade-off, on the otherhand, is possible reduction in static power savings due to more cachelines effectively being fully powered as a result of the powergroupings. For example, suppose we have a 2-Way set associative 8 KBcache with 32-byte lines, this cache would have 64 sets per way as seenin FIG. 4. Now, suppose we create groups of 8 cache lines each such thatthere are 8 power groups per way. Then, a new access to set 9 wouldtrigger full power to all ways of the second group since the wayrequested is not known during the first cache pipe stage i.e., tag RAMaccess. Two cycles later, after hit/miss is determined, the hit way tobe read from, or miss way to be allocated, is kept in full power statewhile the other ways are returned to a nap state if there is no new orpending access to the set and way group. The proposed dNap power groupsdo not span more than a cache way to enable maximum static power savingsafter hit/miss determination.

Power-performance trade off of deterministic napping at the individualcache line allows the maximum number of data RAM lines to be kept inDrowsy state, given individual cache line power can now be controlledindependently. This offers the best static power savings possible inthis architecture because only cache lines offsets to be accessed in theimmediate future are fully powered. But this comes at the expense ofextra hardware required to implement both the MNC and the individual nappower logic per cache line. Memory system architects can choose to groupmultiple cache lines into single memory banks to reduce this hardwareoverhead as needed. Also, to take advantage of the built-in low powerfeature available in some vendor supplied SRAM memory arrays, systemarchitects can choose to fully power a memory array whenever there is atleast an access to any of the lines of the SRAM array.

This eliminates most of the hardware overhead due to napping and wake-upimplementation logic but offers lower static power savings because morecache lines are effectively fully powered. Given there are no readilyavailable tools to evaluate static power consumption by dNap caches, weresolved to using Equation 1 for static power proposed by Butts and Sohi

P _(static) =V _(cc) *N*K _(design) *I _(leak)  (1)

where: V_(cc) is the supply voltage (Full power is 1.0 V, drowsy poweris 0.3 V): N is the number of transistors; K_(design) is a designdependent parameter; and I_(leak) is the leakage current which istechnology dependent. Since both N and K_(design) remain constant inboth drowsy and full power state, and we already have the V_(cc) inthese states, we evaluate the Drowsy state leakage current I_(leak) _(_)_(d) as a function of the full power leakage current I_(leak) usingEquation 2 based on the BSIM3 v3.2 equation for leakage.

$\begin{matrix}{I_{leak} = {\mu_{0}C_{ox}\frac{W}{L}e^{b{({V_{dd} - V_{{dd}\; 0}})}}{V_{t}^{2}\left( {1 - e^{- \frac{V_{dd}}{V_{t}}}} \right)}e^{\frac{{- {V_{th}}} - V_{off}}{n*V_{t}}}}} & (2)\end{matrix}$

where: μ₀ is the zero bias mobility; C_(ox) is gate oxide capacitanceper unit area,

$\frac{W}{L}$

is the transistor aspect ratio; e^(b(V) ^(dd) ^(−V) ^(dd0) ⁾ is the DIBLfactor derived from the curve fitting method; V_(dd0) is the defaultsupply voltage for the technology (V_(dd0) is 1.0 V for 70 nm); V_(t) isthe thermal voltage; V_(th) is threshold voltage which is also afunction of temperature; n is the sub threshold swing coefficient; andV_(off) is an empirically determined BSIM3 parameter which is also afunction of threshold voltage. The quantities μ₀, C_(ox), W/L andV_(dd0) are statically defined parameters. The DIBL factor b, subthreshold swing coefficient, n and V_(off) were derived from the curvefitting method based on the transistor level simulations. We calculatethe leakage current in drowsy mode beak d as a function of I_(leak) asfollows, where V_(dd) _(d) is the drowsy mode voltage which is 0.3 V(i.e. 0.7 V less than V_(dd)) in our simulations.

$\begin{matrix}\begin{matrix}{I_{leak\_ d} = {I_{leak\_ d}*\frac{I_{leak}}{I_{leak}}}} \\{= {\frac{I_{leak\_ d}}{I_{leak}}*{I_{leak}(4)}}}\end{matrix} & (3)\end{matrix}$

Since μ₀, C_(ox),

$\frac{W}{L}$

and V_(dd0) are static parameters, they cancel out yielding Equation 6.

$\begin{matrix}\begin{matrix}{I_{leak\_ d} = {\frac{e^{b{({V_{{dd}_{d}} - V_{{dd}\; 0}})}}}{e^{b{({V_{dd} - V_{{dd}\; 0}})}}}*\frac{1 - e^{- \frac{V_{{dd}_{d}}}{V_{t}}}}{1 - e^{- \frac{V_{dd}}{V_{t}}}}}} \\{= {\frac{e^{b{({{- 0.7} + V_{dd} - V_{{dd}\; 0}})}}}{e^{b{({V_{dd} - V_{{dd}\; 0}})}}}*\frac{1 - e^{- \frac{({{- 0.7} + V_{dd}})}{V_{t}}}}{1 - e^{- \frac{V_{dd}}{V_{t}}}}(6)}}\end{matrix} & (5)\end{matrix}$

The thermal voltage V_(t) is

$\frac{KT}{q}$

where: K is the Boltzman constant 1.38088*10⁻²³; q is 1.602*10⁻¹⁹; and Tis chosen as 350 K rather than the default 300 K in the hot leakage toolto be consistent with Cacti toolset. We retain the default value ofempirical parameter for V_(dd), b=2.0 for the 70 nm node. Equation 6therefore yields Equation 7 after substitution.

I _(leak) _(_) _(d)=0.24659*I _(leak)  (7)

Equation 7 which is consistent with estimations is integrated into Cactifor drowsy leakage power evaluations.

The static (or leakage) power on the dNap architecture was measured andcompared against equivalently configured conventional caches.Simulations were run on 32 KB level 1 (L1) caches with one power enableper line (i.e., w=1), n=3 pipe-line stages and m=4 buffers, and it isexpected that at most N ways (where N ways is set associativity) cachelines will be fully powered due to an access in stage 1 and 2, whileonly 1 cache line in stage 3 and each of the 4 buffers is fully poweredin the presence of an access. This is consistent with simulationresults, which show more than 92% leakage power savings using the dNapcache architecture. FIG. 5 shows the leakage power savings in a 32 KB L1Instruction (L1I) cache with 32-byte cache lines and no power groups,compared to an equivalently sized conventional cache, across multiplecache associativities for various of the SEPC2006 benchmark programs.The increase in leakage power savings as set associativity increases isdue to fewer sets which lead to less dNap hardware overhead as setassociativity increases. This is summarized in Table 1 for an individual32 KB cache and Table 2 at the processor core level. These two tablesalso show that the proposed dNap technique incurs similar hardwareoverheads in the existing DVS technique at higher associativities. Thisis because the number of sets reduce as associativity increases, therebyleading to fewer input and outputs on the decoder logic, andeffectively, fewer OR gates.

TABLE 1 Hardware Overhead Comparison in 32 KB Cache Associativity DVS(%) dNap (%) 1 Way +6.93 +13.54 2 Way +6.61 +10.23 4 Way +6.07 +8.00 8Way +5.26 +6.28 16 Way  +4.15 +4.66

TABLE 2 Hardware Overhead Comparison at Processor Core LevelAssociativity DVS (%) dNap (%) 1 Way +1.63 +3.18 2 Way +1.55 +2.40 4 Way+1.43 +1.88 8 Way +1.24 +1.48 16 Way  +0.97 +1.10

Simulation results indicate more than 92% leakage power savings isachievable with the proposed dNap cache architecture. FIG. 6 shows theleakage power savings in a 32 KB L1 Instruction (L1I) cache with 32-bytelines, compared to an equivalently sized conventional cache, for directmapped through 16 Way set associativity for the same SPEC2006 benchmarkprograms used in FIG. 5. The increase in leakage power savings as setassociativity increases is due to lower percentage hardware increase dueto nap state implementation as set associativity increases as seen inTable 1. This rate of hardware reduction is greater than that of theincrease in fully powered lines due to set associativity seen as insimulation results. This explains the consistent increase in leakagepower savings with increasing associativity.

The dNap scheme shows a slightly better leakage power savings percentagein the L1 Data cache because there were fewer accesses to the L1D in the500 million cycle simulation window across the benchmarks. This allowsthe L1D cache to have a higher proportion of cache lines in nap stateduring program execution.

The significant static power savings (more than 90%) due to the dNaparchitecture does not vary much across different associativities,because the number of fully powered cache lines only varies in the first2 cache pipe-stages before hit/miss way is known. This difference isless than 1% because simulation configurations use 1024 cache lines(i.e., 32 KB cache, 32 byte lines), and the maximum number of extralines in the 16 Way cache configuration are the 15 extra ways in thefirst 2 pipe stages before hit/miss determination. This results in only30 extra cache lines fully powered out of 1024 lines versus the directmapped cache alternative.

Also, there can only be a maximum of “2*N ways+n−2+m” fully poweredlines at any given cycle during program execution in the proposed dNaparchitecture, where N ways is associativity, n is the number ofpipe-stages and m is the number of buffers. This suggests that theperformance of the dNap technique will show only negligible variationsin static/leakage power savings as reflected in FIGS. 5 and 6. Forexample, for N ways=16, n=and m=4, the number of fully powered lines inany cycle, varies from none, to a maximum of 37 of the total 1024 cachelines leading to over 90% power savings.

The static power reduction benefits of deterministic napping is alsoevaluated in low power wearable medical devices. FIG. 7 is focused onthe medical applications of SPEC2006 processor benchmark. These include:hmmer which is used in computational biology to search for patterns inDNA sequences; libquantum, used in human cryptography; namd, a parallelprogram used in the simulation of large bio-molecular systems; povray,used to calculate and simulate the way rays of light travel and hit thehuman eye; and sphinx, a widely known speech recognition program. Thesignificant dNap static power savings do not vary much across differentassociativities and applications, because there can only be a maximumof“2*Nways+m+n−2” fully powered lines at any given cycle during programexecution, where n and m are the pipe-stage and buffer depths,respectively. This is because tag access and comparison occurs in thefirst 2 cycles and all cache ways (2*N ways) must be fully poweredbefore a single hit/miss way is determined. Beyond that, accesses inother pipe-line stages and buffer (i.e., m+n−2) keep only the cachehit/miss way of the referenced set in full power state. This explainsthe minor variations in leakage power savings across applications.

For example, in a 16-way, 32 KB cache, with 32-byte lines, n=3 and m=4,the number of fully powered lines in any cycle, varies from 0 to amaximum of 37 of the total 1024 cache lines. As a result, the maximumpossible variation is 37/1024, which explains the minimal variation inthe static power measurements across the different benchmarks andsimilarly configured L1I and L1D caches. This is unlike dynamic powerwhich significantly varies with applications and access patterns. Theselight static power variations are partly due to multiple in-flightreferences to the same set, and partly due to some unoccupiedpipe-stages or buffer slots in one cache and/or application but not inthe other. Cache architectures with deeper pipe-stages and buffers areexpected to show more power savings variation.

The overall leakage power reduction across the cache hierarchy isfurther evaluated while highlighting the effect of dNap logic anddynamic power due to nap state transitions. This was achieved using thedefault Intel configuration in the sniper simulator, with 64-byte cachelines, 32 KB L1I and L1D with 4-Way and 8-Way associativityrespectively. The L2 and L3 were configured as 8-Way 256 KB and 16-Way32 MB respectively. The McPAT power measurements are summarized in Table3. It shows the overhead due to nap state transitions are negligiblewhile dNap power savings are still significant, with the highest powercontribution due to the always fully powered dNap logic.

TABLE 3 Total Leakage Power in L1s, L2 and L3 dNap Cache Power (W) Conv.Savings Benchmarks Wake logic other (W) (%) hmmer 0.00031 1.86 1.00 9.1668.68 libquantum 0.00006 1.86 0.97 9.16 69.06 namd 0.00014 1.86 0.989.16 68.91 povray 0.00017 1.86 0.97 9.16 68.98 sphinx3 0.00014 1.86 0.989.16 68.96

Leakage (or static) power reduction due to dNaps was also evaluated in amulti core environment. FIG. 8 shows the total static power reduction inall L1 Instruction caches across a 4-core processor for variousbenchmark programs. The result is significant static power reductionwhich does not exceed those of the DVS technique. This is because thecompared flavor of DVS technique waits until access reaches the data RAMbefore waking up the RAMS; as a result, the DVS is in lower power statefor longer periods than the dNap technique. The dNap however, does notincur the performance penalty of the DVS for the static power savings itoffers.

FIG. 9 shows a similar trend in static power savings at the 4-coreprocessor level for the same benchmark programs used in FIG. 8. The onlydifference being a lower static power reduction, due to other processorcomponents not participating in deterministic napping.

It is worth noting that more cache lines per dNap group leads to fewerwake-up transitions due to more fully powered lines over the course ofprogram execution. It was also observed that all power groups in allbenchmarks evaluated in this work, completely transitioned in and out ofnap state within a single clock cycle.

Both the Simple scalar toolset and Cacti v6.5 toolset was used as thebasis of the simulator development for static power evaluation. Whilethere are multiple flavors of these tools, none completely model thearchitectural technique shown in this invention. Therefore, a robustsimulator was developed using both existing tools as basis. The state ofall cache lines are tracked per cycle and the static power for each lineis computed using Equations 1 and 7. The total static energy for 500million cycles of simulation was collected for different 32 KB cacheconfigurations on SPEC2006 benchmarks and compared with conventionalnon-drowsy caches. Table 4 gives a brief summary of the defaultconfigurations used across all of the simulations.

TABLE 4 Simulation Configuration Parameters Value Instruction FetchQueue Size 4 Instructions Instruction Decode Width 4 Instructions PerCycle Instruction Issue 4 Instructions Per Cycle L1 Instruction CacheLatency 3 cycles L1 Data Cache Latency 3 cycles L2 Unified Cache Latency11 cycles Main Memory Latency 26 cycles TLB Miss Latency 30 cyclesMemory Access Width 32 Bytes

What is claimed is:
 1. A cache memory system, comprising: a cache memoryoperable to store data in a plurality of locations defined by aplurality of addresses and divided into a plurality of cache ways; amemory nap controller operable to track in-flight cache accesses andfurther operable to control the desired power state of a plurality ofdata RAM lines; a processing block operable to group a plurality ofcontiguous cache lines into a single power group.
 2. The cache memorysystem of claim 1, wherein: the power state of said data RAM lines isdetermined by the memory nap controller based on all current anoutstanding data RAM accesses in all pipeline stages and buffers.
 3. Thecache memory system of claim 1, wherein: the memory nap controller isoperable to transition the data RAM lines to a fully powered operatingstate or to a low power nap state.
 4. The cache memory system of claim1, wherein: the memory nap controller is operable to complete thetransition of the data RAM lines to a fully powered state ahead of theactual data RAM access without any additional latency incurred at thetime of the access.
 5. The cache memory system of claim 1, wherein: thememory nap controller is operable to place the cache lines that will notbe used in the immediate future in a low power state, and keep only thecache lines determined to be accessed in the immediate future in a fullypowered on state.
 6. The cache memory system of claim 1, wherein: thememory nap controller is operable, after a hit/miss determination hasselected a single cache way to place all other ways in a low powerstate.
 7. The cache memory system of claim 6, wherein: the memory napcontroller will, in the case of a new access to a cache way that istransitioning to a low power state maintain said cache way in a poweredup state until the access has completed.